@article {607,
	title = {Assessment of NER solutions against the first and second CALBC Silver Standard Corpus.},
	journal = {Journal of biomedical semantics},
	volume = {2 Suppl 5},
	year = {2011},
	month = {2011},
	pages = {S11},
	abstract = {BACKGROUND: Competitions in text mining have been used to measure the performance of automatic text processing solutions against a manually annotated gold standard corpus (GSC). The preparation of the GSC is time-consuming and costly and the final corpus consists at the most of a few thousand documents annotated with a limited set of semantic groups. To overcome these shortcomings, the CALBC project partners (PPs) have produced a large-scale annotated biomedical corpus with four different semantic groups through the harmonisation of annotations from automatic text mining solutions, the first version of the Silver Standard Corpus (SSC-I). The four semantic groups are chemical entities and drugs (CHED), genes and proteins (PRGE), diseases and disorders (DISO) and species (SPE). This corpus has been used for the First CALBC Challenge asking the participants to annotate the corpus with their text processing solutions.

RESULTS: All four PPs from the CALBC project and in addition, 12 challenge participants (CPs) contributed annotated data sets for an evaluation against the SSC-I. CPs could ignore the training data and deliver the annotations from their genuine annotation system, or could train a machine-learning approach on the provided pre-annotated data. In general, the performances of the annotation solutions were lower for entities from the categories CHED and PRGE in comparison to the identification of entities categorized as DISO and SPE. The best performance over all semantic groups were achieved from two annotation solutions that have been trained on the SSC-I.The data sets from participants were used to generate the harmonised Silver Standard Corpus II (SSC-II), if the participant did not make use of the annotated data set from the SSC-I for training purposes. The performances of the participants{\textquoteright} solutions were again measured against the SSC-II. The performances of the annotation solutions showed again better results for DISO and SPE in comparison to CHED and PRGE.

CONCLUSIONS: The SSC-I delivers a large set of annotations (1,121,705) for a large number of documents (100,000 Medline abstracts). The annotations cover four different semantic groups and are sufficiently homogeneous to be reproduced with a trained classifier leading to an average F-measure of 85\%. Benchmarking the annotation solutions against the SSC-II leads to better performance for the CPs{\textquoteright} annotation solutions in comparison to the SSC-I.},
	issn = {2041-1480},
	doi = {10.1186/2041-1480-2-S5-S11},
	url = {http://www.jbiomedsem.com/content/2/S5/S11/},
	attachments = {https://www.semanticsoftware.info/system/files/2041-1480-2-S5-S11.pdf},
	author = {Dietrich Rebholz-Schuhmann and Jimeno Yepes, Antonio and Chen Li and Senay Kafkas and Ian Lewin and Ning Kang and Peter Corbett and David Milward and Ekaterina Buyko and Elena Beisswanger and Kerstin Hornbostel and Alexandre Kouznetsov and Ren{\'e} Witte and Jonas B. Laurila and Christopher J.O. Baker and Kuo, Cheng-Ju and Clematide, Simone and Fabio Rinaldi and Rich{\'a}rd Farkas and Gy{\"o}rgy M{\'o}ra and Kazuo Hara and Furlong, Laura I and Michael Rautschka and Neves, Mariana Lara and Pascual-Montano, Alberto and Qi Wei and Nigel Collier and Chowdhury, Md Faisal Mahbub and Alberto Lavelli and Berlanga, Rafael and Roser Morante and Vincent Van Asch and Walter Daelemans and Jos{\'e} Lu{\'\i}s Marina and van Mulligen, Erik and Kors, Jan and Udo Hahn}
}
